Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory626.5 KiB
Average record size in memory641.5 B

Variable types

Text3
Numeric4
Categorical5
DateTime1
Boolean1

Alerts

Transaction ID has unique values Unique

Reproduction

Analysis started2025-02-14 13:06:13.937430
Analysis finished2025-02-14 13:06:16.621609
Duration2.68 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Transaction ID
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.5 KiB
2025-02-14T22:06:16.742608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowTXN9520068950
2nd rowTXN9412011085
3rd rowTXN4407425052
4th rowTXN2214150284
5th rowTXN4247571145
ValueCountFrequency (%)
txn9520068950 1
 
0.1%
txn5194651812 1
 
0.1%
txn3914566506 1
 
0.1%
txn7538176865 1
 
0.1%
txn4407425052 1
 
0.1%
txn2214150284 1
 
0.1%
txn4247571145 1
 
0.1%
txn2515439857 1
 
0.1%
txn2169752734 1
 
0.1%
txn3109277527 1
 
0.1%
Other values (990) 990
99.0%
2025-02-14T22:06:17.027128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 1058
 
8.1%
6 1026
 
7.9%
2 1023
 
7.9%
4 1021
 
7.9%
8 1020
 
7.8%
7 1002
 
7.7%
T 1000
 
7.7%
X 1000
 
7.7%
N 1000
 
7.7%
1 999
 
7.7%
Other values (3) 2851
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 1058
 
8.1%
6 1026
 
7.9%
2 1023
 
7.9%
4 1021
 
7.9%
8 1020
 
7.8%
7 1002
 
7.7%
T 1000
 
7.7%
X 1000
 
7.7%
N 1000
 
7.7%
1 999
 
7.7%
Other values (3) 2851
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 1058
 
8.1%
6 1026
 
7.9%
2 1023
 
7.9%
4 1021
 
7.9%
8 1020
 
7.8%
7 1002
 
7.7%
T 1000
 
7.7%
X 1000
 
7.7%
N 1000
 
7.7%
1 999
 
7.7%
Other values (3) 2851
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 1058
 
8.1%
6 1026
 
7.9%
2 1023
 
7.9%
4 1021
 
7.9%
8 1020
 
7.8%
7 1002
 
7.7%
T 1000
 
7.7%
X 1000
 
7.7%
N 1000
 
7.7%
1 999
 
7.7%
Other values (3) 2851
21.9%
Distinct994
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
2025-02-14T22:06:17.247130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique988 ?
Unique (%)98.8%

Sample

1st rowACC14994
2nd rowACC58958
3rd rowACC56321
4th rowACC48650
5th rowACC60921
ValueCountFrequency (%)
acc37810 2
 
0.2%
acc89865 2
 
0.2%
acc75741 2
 
0.2%
acc71245 2
 
0.2%
acc50985 2
 
0.2%
acc82828 2
 
0.2%
acc10284 1
 
0.1%
acc39081 1
 
0.1%
acc62917 1
 
0.1%
acc11156 1
 
0.1%
Other values (984) 984
98.4%
2025-02-14T22:06:17.554370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 2000
25.0%
A 1000
12.5%
8 536
 
6.7%
4 524
 
6.6%
3 520
 
6.5%
5 519
 
6.5%
2 510
 
6.4%
7 507
 
6.3%
9 499
 
6.2%
1 492
 
6.2%
Other values (2) 893
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2000
25.0%
A 1000
12.5%
8 536
 
6.7%
4 524
 
6.6%
3 520
 
6.5%
5 519
 
6.5%
2 510
 
6.4%
7 507
 
6.3%
9 499
 
6.2%
1 492
 
6.2%
Other values (2) 893
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2000
25.0%
A 1000
12.5%
8 536
 
6.7%
4 524
 
6.6%
3 520
 
6.5%
5 519
 
6.5%
2 510
 
6.4%
7 507
 
6.3%
9 499
 
6.2%
1 492
 
6.2%
Other values (2) 893
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2000
25.0%
A 1000
12.5%
8 536
 
6.7%
4 524
 
6.6%
3 520
 
6.5%
5 519
 
6.5%
2 510
 
6.4%
7 507
 
6.3%
9 499
 
6.2%
1 492
 
6.2%
Other values (2) 893
11.2%
Distinct994
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
2025-02-14T22:06:17.784566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique988 ?
Unique (%)98.8%

Sample

1st rowACC16656
2nd rowACC32826
3rd rowACC92481
4th rowACC76457
5th rowACC11419
ValueCountFrequency (%)
acc51253 2
 
0.2%
acc85405 2
 
0.2%
acc76710 2
 
0.2%
acc51744 2
 
0.2%
acc36934 2
 
0.2%
acc17647 2
 
0.2%
acc93536 1
 
0.1%
acc77317 1
 
0.1%
acc57086 1
 
0.1%
acc27800 1
 
0.1%
Other values (984) 984
98.4%
2025-02-14T22:06:18.094250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 2000
25.0%
A 1000
12.5%
3 553
 
6.9%
1 539
 
6.7%
7 523
 
6.5%
6 511
 
6.4%
8 510
 
6.4%
2 506
 
6.3%
5 501
 
6.3%
4 499
 
6.2%
Other values (2) 858
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2000
25.0%
A 1000
12.5%
3 553
 
6.9%
1 539
 
6.7%
7 523
 
6.5%
6 511
 
6.4%
8 510
 
6.4%
2 506
 
6.3%
5 501
 
6.3%
4 499
 
6.2%
Other values (2) 858
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2000
25.0%
A 1000
12.5%
3 553
 
6.9%
1 539
 
6.7%
7 523
 
6.5%
6 511
 
6.4%
8 510
 
6.4%
2 506
 
6.3%
5 501
 
6.3%
4 499
 
6.2%
Other values (2) 858
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2000
25.0%
A 1000
12.5%
3 553
 
6.9%
1 539
 
6.7%
7 523
 
6.5%
6 511
 
6.4%
8 510
 
6.4%
2 506
 
6.3%
5 501
 
6.3%
4 499
 
6.2%
Other values (2) 858
10.7%

Transaction Amount
Real number (ℝ)

Distinct998
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean771.16529
Minimum51.89
Maximum1497.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-14T22:06:18.229249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.89
5-th percentile128.5895
Q1423.3475
median761.655
Q31122.6725
95-th percentile1429.7395
Maximum1497.76
Range1445.87
Interquartile range (IQR)699.325

Descriptive statistics

Standard deviation411.01925
Coefficient of variation (CV)0.53298463
Kurtosis-1.1621161
Mean771.16529
Median Absolute Deviation (MAD)351.81
Skewness0.0099688101
Sum771165.29
Variance168936.82
MonotonicityNot monotonic
2025-02-14T22:06:18.343251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1449.24 2
 
0.2%
802.61 2
 
0.2%
495.9 1
 
0.1%
1037.73 1
 
0.1%
440.3 1
 
0.1%
293.9 1
 
0.1%
55.17 1
 
0.1%
1133.66 1
 
0.1%
630.84 1
 
0.1%
427.26 1
 
0.1%
Other values (988) 988
98.8%
ValueCountFrequency (%)
51.89 1
0.1%
53.53 1
0.1%
54.96 1
0.1%
55.17 1
0.1%
56.93 1
0.1%
58.13 1
0.1%
58.69 1
0.1%
59.49 1
0.1%
59.57 1
0.1%
60.88 1
0.1%
ValueCountFrequency (%)
1497.76 1
0.1%
1495.01 1
0.1%
1494.2 1
0.1%
1494.03 1
0.1%
1494.01 1
0.1%
1493.86 1
0.1%
1493.27 1
0.1%
1488.93 1
0.1%
1485.37 1
0.1%
1485.22 1
0.1%

Transaction Type
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size63.9 KiB
Transfer
374 
Deposit
316 
Withdrawal
310 

Length

Max length10
Median length8
Mean length8.304
Min length7

Characters and Unicode

Total characters8304
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeposit
2nd rowWithdrawal
3rd rowWithdrawal
4th rowTransfer
5th rowDeposit

Common Values

ValueCountFrequency (%)
Transfer 374
37.4%
Deposit 316
31.6%
Withdrawal 310
31.0%

Length

2025-02-14T22:06:18.460250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T22:06:18.556208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
transfer 374
37.4%
deposit 316
31.6%
withdrawal 310
31.0%

Most occurring characters

ValueCountFrequency (%)
r 1058
12.7%
a 994
12.0%
s 690
 
8.3%
e 690
 
8.3%
i 626
 
7.5%
t 626
 
7.5%
T 374
 
4.5%
n 374
 
4.5%
f 374
 
4.5%
p 316
 
3.8%
Other values (7) 2182
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1058
12.7%
a 994
12.0%
s 690
 
8.3%
e 690
 
8.3%
i 626
 
7.5%
t 626
 
7.5%
T 374
 
4.5%
n 374
 
4.5%
f 374
 
4.5%
p 316
 
3.8%
Other values (7) 2182
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1058
12.7%
a 994
12.0%
s 690
 
8.3%
e 690
 
8.3%
i 626
 
7.5%
t 626
 
7.5%
T 374
 
4.5%
n 374
 
4.5%
f 374
 
4.5%
p 316
 
3.8%
Other values (7) 2182
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1058
12.7%
a 994
12.0%
s 690
 
8.3%
e 690
 
8.3%
i 626
 
7.5%
t 626
 
7.5%
T 374
 
4.5%
n 374
 
4.5%
f 374
 
4.5%
p 316
 
3.8%
Other values (7) 2182
26.3%
Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2025-01-17 10:01:00
Maximum2025-01-17 11:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-14T22:06:18.653204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:18.764204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
Failed
513 
Success
487 

Length

Max length7
Median length6
Mean length6.487
Min length6

Characters and Unicode

Total characters6487
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFailed
2nd rowSuccess
3rd rowFailed
4th rowSuccess
5th rowSuccess

Common Values

ValueCountFrequency (%)
Failed 513
51.3%
Success 487
48.7%

Length

2025-02-14T22:06:18.872208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T22:06:18.944783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
failed 513
51.3%
success 487
48.7%

Most occurring characters

ValueCountFrequency (%)
e 1000
15.4%
c 974
15.0%
s 974
15.0%
F 513
7.9%
a 513
7.9%
i 513
7.9%
l 513
7.9%
d 513
7.9%
S 487
7.5%
u 487
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6487
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1000
15.4%
c 974
15.0%
s 974
15.0%
F 513
7.9%
a 513
7.9%
i 513
7.9%
l 513
7.9%
d 513
7.9%
S 487
7.5%
u 487
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6487
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1000
15.4%
c 974
15.0%
s 974
15.0%
F 513
7.9%
a 513
7.9%
i 513
7.9%
l 513
7.9%
d 513
7.9%
S 487
7.5%
u 487
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6487
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1000
15.4%
c 974
15.0%
s 974
15.0%
F 513
7.9%
a 513
7.9%
i 513
7.9%
l 513
7.9%
d 513
7.9%
S 487
7.5%
u 487
7.5%

Fraud Flag
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
519 
True
481 
ValueCountFrequency (%)
False 519
51.9%
True 481
48.1%
2025-02-14T22:06:19.125778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct36
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size75.4 KiB
48.8566 N, 139.6917 W
 
42
51.5074 N, 0.1278 W
 
38
55.7558 N, 37.6173 W
 
37
55.7558 N, -74.006 W
 
37
35.6895 N, 2.3522 W
 
32
Other values (31)
814 

Length

Max length22
Median length21
Mean length20.122
Min length19

Characters and Unicode

Total characters20122
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row34.0522 N, -74.006 W
2nd row35.6895 N, -118.2437 W
3rd row48.8566 N, 2.3522 W
4th row34.0522 N, -74.006 W
5th row55.7558 N, 37.6173 W

Common Values

ValueCountFrequency (%)
48.8566 N, 139.6917 W 42
 
4.2%
51.5074 N, 0.1278 W 38
 
3.8%
55.7558 N, 37.6173 W 37
 
3.7%
55.7558 N, -74.006 W 37
 
3.7%
35.6895 N, 2.3522 W 32
 
3.2%
55.7558 N, 2.3522 W 32
 
3.2%
35.6895 N, -118.2437 W 31
 
3.1%
51.5074 N, -118.2437 W 31
 
3.1%
48.8566 N, 0.1278 W 31
 
3.1%
34.0522 N, 139.6917 W 31
 
3.1%
Other values (26) 658
65.8%

Length

2025-02-14T22:06:19.211779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 1000
25.0%
w 1000
25.0%
0.1278 183
 
4.6%
48.8566 179
 
4.5%
139.6917 176
 
4.4%
2.3522 175
 
4.4%
55.7558 170
 
4.2%
35.6895 169
 
4.2%
34.0522 166
 
4.2%
51.5074 165
 
4.1%
Other values (4) 617
15.4%

Most occurring characters

ValueCountFrequency (%)
3000
14.9%
. 2000
9.9%
5 1868
 
9.3%
7 1467
 
7.3%
2 1343
 
6.7%
1 1311
 
6.5%
8 1183
 
5.9%
3 1150
 
5.7%
6 1017
 
5.1%
N 1000
 
5.0%
Other values (6) 4783
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3000
14.9%
. 2000
9.9%
5 1868
 
9.3%
7 1467
 
7.3%
2 1343
 
6.7%
1 1311
 
6.5%
8 1183
 
5.9%
3 1150
 
5.7%
6 1017
 
5.1%
N 1000
 
5.0%
Other values (6) 4783
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3000
14.9%
. 2000
9.9%
5 1868
 
9.3%
7 1467
 
7.3%
2 1343
 
6.7%
1 1311
 
6.5%
8 1183
 
5.9%
3 1150
 
5.7%
6 1017
 
5.1%
N 1000
 
5.0%
Other values (6) 4783
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3000
14.9%
. 2000
9.9%
5 1868
 
9.3%
7 1467
 
7.3%
2 1343
 
6.7%
1 1311
 
6.5%
8 1183
 
5.9%
3 1150
 
5.7%
6 1017
 
5.1%
N 1000
 
5.0%
Other values (6) 4783
23.8%

Device Used
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
Mobile
521 
Desktop
479 

Length

Max length7
Median length6
Mean length6.479
Min length6

Characters and Unicode

Total characters6479
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesktop
2nd rowMobile
3rd rowMobile
4th rowMobile
5th rowMobile

Common Values

ValueCountFrequency (%)
Mobile 521
52.1%
Desktop 479
47.9%

Length

2025-02-14T22:06:19.302106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T22:06:19.376104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mobile 521
52.1%
desktop 479
47.9%

Most occurring characters

ValueCountFrequency (%)
o 1000
15.4%
e 1000
15.4%
M 521
8.0%
b 521
8.0%
i 521
8.0%
l 521
8.0%
D 479
7.4%
s 479
7.4%
k 479
7.4%
t 479
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6479
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1000
15.4%
e 1000
15.4%
M 521
8.0%
b 521
8.0%
i 521
8.0%
l 521
8.0%
D 479
7.4%
s 479
7.4%
k 479
7.4%
t 479
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6479
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1000
15.4%
e 1000
15.4%
M 521
8.0%
b 521
8.0%
i 521
8.0%
l 521
8.0%
D 479
7.4%
s 479
7.4%
k 479
7.4%
t 479
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6479
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1000
15.4%
e 1000
15.4%
M 521
8.0%
b 521
8.0%
i 521
8.0%
l 521
8.0%
D 479
7.4%
s 479
7.4%
k 479
7.4%
t 479
7.4%

Network Slice ID
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
Slice2
340 
Slice3
337 
Slice1
323 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSlice3
2nd rowSlice2
3rd rowSlice1
4th rowSlice3
5th rowSlice3

Common Values

ValueCountFrequency (%)
Slice2 340
34.0%
Slice3 337
33.7%
Slice1 323
32.3%

Length

2025-02-14T22:06:19.453108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T22:06:19.525106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
slice2 340
34.0%
slice3 337
33.7%
slice1 323
32.3%

Most occurring characters

ValueCountFrequency (%)
S 1000
16.7%
l 1000
16.7%
i 1000
16.7%
c 1000
16.7%
e 1000
16.7%
2 340
 
5.7%
3 337
 
5.6%
1 323
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1000
16.7%
l 1000
16.7%
i 1000
16.7%
c 1000
16.7%
e 1000
16.7%
2 340
 
5.7%
3 337
 
5.6%
1 323
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1000
16.7%
l 1000
16.7%
i 1000
16.7%
c 1000
16.7%
e 1000
16.7%
2 340
 
5.7%
3 337
 
5.6%
1 323
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1000
16.7%
l 1000
16.7%
i 1000
16.7%
c 1000
16.7%
e 1000
16.7%
2 340
 
5.7%
3 337
 
5.6%
1 323
 
5.4%

Latency (ms)
Real number (ℝ)

Distinct18
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.688
Minimum3
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-14T22:06:19.616343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q17
median12
Q316
95-th percentile20
Maximum20
Range17
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.131958
Coefficient of variation (CV)0.43907923
Kurtosis-1.1932584
Mean11.688
Median Absolute Deviation (MAD)4.5
Skewness-0.035953025
Sum11688
Variance26.336993
MonotonicityNot monotonic
2025-02-14T22:06:19.711634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15 74
 
7.4%
19 65
 
6.5%
7 64
 
6.4%
10 63
 
6.3%
6 63
 
6.3%
17 62
 
6.2%
11 61
 
6.1%
9 59
 
5.9%
14 54
 
5.4%
13 54
 
5.4%
Other values (8) 381
38.1%
ValueCountFrequency (%)
3 51
5.1%
4 49
4.9%
5 42
4.2%
6 63
6.3%
7 64
6.4%
8 44
4.4%
9 59
5.9%
10 63
6.3%
11 61
6.1%
12 43
4.3%
ValueCountFrequency (%)
20 53
5.3%
19 65
6.5%
18 51
5.1%
17 62
6.2%
16 48
4.8%
15 74
7.4%
14 54
5.4%
13 54
5.4%
12 43
4.3%
11 61
6.1%

Slice Bandwidth (Mbps)
Real number (ℝ)

Distinct199
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.511
Minimum50
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-14T22:06:19.827290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile61
Q198
median148
Q3198.25
95-th percentile240.05
Maximum250
Range200
Interquartile range (IQR)100.25

Descriptive statistics

Standard deviation57.78634
Coefficient of variation (CV)0.38910478
Kurtosis-1.2178153
Mean148.511
Median Absolute Deviation (MAD)50
Skewness0.077265804
Sum148511
Variance3339.2611
MonotonicityNot monotonic
2025-02-14T22:06:19.954808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132 12
 
1.2%
99 11
 
1.1%
150 10
 
1.0%
94 10
 
1.0%
247 9
 
0.9%
173 9
 
0.9%
153 9
 
0.9%
244 9
 
0.9%
181 9
 
0.9%
214 9
 
0.9%
Other values (189) 903
90.3%
ValueCountFrequency (%)
50 1
 
0.1%
51 2
 
0.2%
52 5
0.5%
53 2
 
0.2%
54 7
0.7%
55 5
0.5%
56 6
0.6%
57 5
0.5%
58 3
0.3%
59 6
0.6%
ValueCountFrequency (%)
250 6
0.6%
249 3
 
0.3%
248 6
0.6%
247 9
0.9%
246 3
 
0.3%
245 1
 
0.1%
244 9
0.9%
243 8
0.8%
242 1
 
0.1%
241 4
0.4%

PIN Code
Real number (ℝ)

Distinct948
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5458.666
Minimum1000
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-14T22:06:20.068806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1406.7
Q13281.75
median5385.5
Q37535
95-th percentile9622
Maximum9999
Range8999
Interquartile range (IQR)4253.25

Descriptive statistics

Standard deviation2603.0365
Coefficient of variation (CV)0.47686311
Kurtosis-1.1508739
Mean5458.666
Median Absolute Deviation (MAD)2129
Skewness0.03867808
Sum5458666
Variance6775798.8
MonotonicityNot monotonic
2025-02-14T22:06:20.188806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3751 3
 
0.3%
3996 2
 
0.2%
4478 2
 
0.2%
4457 2
 
0.2%
7420 2
 
0.2%
2084 2
 
0.2%
5084 2
 
0.2%
6648 2
 
0.2%
9950 2
 
0.2%
6972 2
 
0.2%
Other values (938) 979
97.9%
ValueCountFrequency (%)
1000 2
0.2%
1009 1
0.1%
1010 1
0.1%
1035 1
0.1%
1038 1
0.1%
1039 1
0.1%
1061 1
0.1%
1066 1
0.1%
1076 1
0.1%
1094 1
0.1%
ValueCountFrequency (%)
9999 1
0.1%
9997 1
0.1%
9996 1
0.1%
9988 1
0.1%
9983 1
0.1%
9976 1
0.1%
9961 1
0.1%
9953 2
0.2%
9952 1
0.1%
9950 2
0.2%

Interactions

2025-02-14T22:06:15.901830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:14.444430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:14.888788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:15.350790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:15.995829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:14.569431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:15.035797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:15.473641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:16.106828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:14.686431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:15.145793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:15.710769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:16.231828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:14.796427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:15.244786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-14T22:06:15.807301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-02-14T22:06:20.263939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Device UsedFraud FlagGeolocation (Latitude/Longitude)Latency (ms)Network Slice IDPIN CodeSlice Bandwidth (Mbps)Transaction AmountTransaction StatusTransaction Type
Device Used1.0000.0550.0000.0000.0770.0000.0310.0000.0000.000
Fraud Flag0.0551.0000.1010.0000.0260.0870.0460.0510.0000.047
Geolocation (Latitude/Longitude)0.0000.1011.0000.0630.0750.0540.0430.0250.0760.000
Latency (ms)0.0000.0000.0631.0000.0000.0020.065-0.0270.0000.000
Network Slice ID0.0770.0260.0750.0001.0000.0340.0620.0000.0000.016
PIN Code0.0000.0870.0540.0020.0341.000-0.002-0.0450.0300.000
Slice Bandwidth (Mbps)0.0310.0460.0430.0650.062-0.0021.0000.0080.0000.041
Transaction Amount0.0000.0510.025-0.0270.000-0.0450.0081.0000.0640.056
Transaction Status0.0000.0000.0760.0000.0000.0300.0000.0641.0000.000
Transaction Type0.0000.0470.0000.0000.0160.0000.0410.0560.0001.000

Missing values

2025-02-14T22:06:16.360608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-14T22:06:16.537611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Transaction IDSender Account IDReceiver Account IDTransaction AmountTransaction TypeTimestampTransaction StatusFraud FlagGeolocation (Latitude/Longitude)Device UsedNetwork Slice IDLatency (ms)Slice Bandwidth (Mbps)PIN Code
0TXN9520068950ACC14994ACC16656495.90Deposit2025-01-17 10:14:00FailedTrue34.0522 N, -74.006 WDesktopSlice3101793075
1TXN9412011085ACC58958ACC32826529.62Withdrawal2025-01-17 10:51:00SuccessFalse35.6895 N, -118.2437 WMobileSlice211892369
2TXN4407425052ACC56321ACC92481862.47Withdrawal2025-01-17 10:50:00FailedFalse48.8566 N, 2.3522 WMobileSlice14538039
3TXN2214150284ACC48650ACC764571129.88Transfer2025-01-17 10:56:00SuccessTrue34.0522 N, -74.006 WMobileSlice3101276374
4TXN4247571145ACC60921ACC11419933.24Deposit2025-01-17 10:25:00SuccessTrue55.7558 N, 37.6173 WMobileSlice3201918375
5TXN2515439857ACC18381ACC76710449.56Withdrawal2025-01-17 10:01:00FailedFalse35.6895 N, 139.6917 WDesktopSlice1141914779
6TXN2169752734ACC43792ACC5110572.97Withdrawal2025-01-17 10:25:00SuccessTrue55.7558 N, 2.3522 WMobileSlice252164347
7TXN3109277527ACC48227ACC935361135.80Deposit2025-01-17 10:56:00FailedTrue48.8566 N, -118.2437 WMobileSlice211698607
8TXN4541918858ACC10284ACC240181154.59Withdrawal2025-01-17 10:38:00SuccessFalse40.7128 N, -118.2437 WMobileSlice261511841
9TXN4229370499ACC11531ACC53580683.48Transfer2025-01-17 10:55:00SuccessTrue34.0522 N, 2.3522 WMobileSlice1192134495
Transaction IDSender Account IDReceiver Account IDTransaction AmountTransaction TypeTimestampTransaction StatusFraud FlagGeolocation (Latitude/Longitude)Device UsedNetwork Slice IDLatency (ms)Slice Bandwidth (Mbps)PIN Code
990TXN2679491739ACC69673ACC60940919.35Deposit2025-01-17 10:52:00FailedFalse48.8566 N, 37.6173 WMobileSlice1172108981
991TXN6073923771ACC31273ACC751601285.80Withdrawal2025-01-17 10:17:00FailedTrue34.0522 N, 2.3522 WDesktopSlice27874938
992TXN9550044170ACC45335ACC103631098.28Deposit2025-01-17 10:39:00SuccessTrue35.6895 N, 139.6917 WDesktopSlice3121992084
993TXN2106828046ACC68992ACC32882236.29Transfer2025-01-17 10:43:00SuccessFalse51.5074 N, -118.2437 WDesktopSlice2201698004
994TXN9649254386ACC13948ACC71806378.90Transfer2025-01-17 10:36:00SuccessFalse48.8566 N, 0.1278 WMobileSlice2101044527
995TXN7395336359ACC79886ACC149031340.76Transfer2025-01-17 10:49:00SuccessFalse35.6895 N, -118.2437 WMobileSlice2192249766
996TXN2215717837ACC95972ACC50750483.36Withdrawal2025-01-17 11:00:00FailedTrue55.7558 N, 37.6173 WMobileSlice212561009
997TXN1676848215ACC95938ACC18507199.81Withdrawal2025-01-17 10:54:00SuccessFalse34.0522 N, 139.6917 WDesktopSlice1141519301
998TXN2479413280ACC76523ACC952341341.86Transfer2025-01-17 10:59:00SuccessTrue51.5074 N, 139.6917 WMobileSlice38954038
999TXN3992032184ACC16789ACC21980495.36Transfer2025-01-17 10:02:00FailedFalse55.7558 N, -74.006 WMobileSlice251559888